Suchergebnisse - "\"(conditional OR conditioning) variational (autoencoder（cvae）*\" OR autoencoder（cae）*\")~"

Heartbeat detection and personal authentication using a 60 GHz Doppler sensor.

Autoren: Asano, Takuma Izumi, Shintaro Kawaguchi, Hiroshi

Quelle: Frontiers in Digital Health; 2025, p1-12, 12p

Schlagwörter: PATIENT monitoring equipment, BIOLOGICAL models, RESEARCH funding, SOUND spectrography, BIOMETRY, HEART beat, ELECTROCARDIOGRAPHY, SECURITY systems, TECHNOLOGY, PATIENT monitoring, MICROWAVES, WAVE analysis, ALGORITHMS

Fast Prediction of Airfoil Aerodynamic Characteristics Based on a Combined Autoencoder.

Autoren: Wang, Xu Qian, Weiqi Zhao, Tun et al.

Quelle: Symmetry (20738994); Jul2024, Vol. 16 Issue 7, p791, 17p

Schlagwörter: WIND tunnel testing, LATENT variables, REYNOLDS number, AEROFOILS, NETWORK performance

Deep Learning for Time Series Anomaly Detection: A Survey.

Autoren: Zamanzadeh Darban, Zahra Webb, Geoffrey I. Pan, Shirui et al.

Quelle: ACM Computing Surveys; Jan2025, Vol. 57 Issue 1, p1-42, 42p

Schlagwörter: ARTIFICIAL neural networks, ARTIFICIAL intelligence, REINFORCEMENT learning, CONVOLUTIONAL neural networks, GRAPH neural networks, DEEP learning, SUPERVISED learning

Wood-species identification based on terahertz spectral data augmentation and pseudo-label guided deep clustering.

Autoren: Wang, Yuan Wang, Zhi-Gang He, Yi-Hao et al.

Quelle: Wood Material Science & Engineering; Oct2024, Vol. 19 Issue 5, p1004-1014, 11p

Schlagwörter: WOOD, DATA augmentation, MACHINE learning, PROBLEM solving, TERAHERTZ spectroscopy, GRISELINIA littoralis

Deep Neural Network with Anomaly Detection for Single-Cycle Battery Lifetime Prediction.

Autoren: Lee, Junghwan Wang, Longda Jung, Hoseok et al.

Quelle: Batteries; Aug2025, Vol. 11 Issue 8, p288, 22p

Schlagwörter: AUTOENCODERS, DEEP learning, ARTIFICIAL neural networks, DATA integrity, REMAINING useful life, OUTLIER detection, ELECTRIC vehicles

A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment.

Autoren: Elahi, Mahboob Afolaranmi, Samuel Olaiya Martinez Lastra, Jose Luis et al.

Quelle: Discover Artificial Intelligence; 12/7/2023, Vol. 3 Issue 1, p1-78, 78p

Schlagwörter: GENERATIVE adversarial networks, ARTIFICIAL intelligence, CONVOLUTIONAL neural networks, AGILE software development, DIGITAL transformation, INDUSTRIAL equipment, RADIOSTEREOMETRY

Extensive framework based on novel convolutional and variational autoencoder based on maximization of mutual information for anomaly detection.

Autoren: Yu, Qien Kavitha, Muthu Subash Kurita, Takio

Quelle: Neural Computing & Applications; Oct2021, Vol. 33 Issue 20, p13785-13807, 23p

Schlagwörter: ANOMALY detection (Computer security), INTRUSION detection systems (Computer security), VECTOR data, BIVECTORS, CHANNEL coding, NETWORK performance

Mixture of experts with convolutional and variational autoencoders for anomaly detection.

Autoren: Yu, Qien Kavitha, Muthu Subash Kurita, Takio

Quelle: Applied Intelligence; Jun2021, Vol. 51 Issue 6, p3241-3254, 14p

Schlagwörter: ANOMALY detection (Computer security), DATA structures, MIXTURES, LOGISTIC regression analysis, DATABASES, SIGNAL convolution

Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection.

Autoren: Lightbody, Dominic Ngo, Duc-Minh Temko, Andriy et al.

Quelle: Future Internet; Mar2024, Vol. 16 Issue 3, p88, 38p

Schlagwörter: INTRUSION detection systems (Computer security), COMPUTER network traffic, INTERNET of things, CYBERTERRORISM, DRAGONS, ARTIFICIAL intelligence

Bridging Deep Convolutional Autoencoders and Ensemble Smoothers for Improved Estimation of Channelized Reservoirs.

Autoren: Sebacher, Bogdan Toma, Stefan Adrian

Quelle: Mathematical Geosciences; Jul2022, Vol. 54 Issue 5, p903-939, 37p

Autoencoders and their applications in machine learning: a survey.

Autoren: Berahmand, Kamal Daneshfar, Fatemeh Salehi, Elaheh Sadat et al.

Quelle: Artificial Intelligence Review; Feb2024, Vol. 57 Issue 2, p1-52, 52p

Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review.

Autoren: Wahlquist, Scott Ali, Amir

Quelle: Applied Sciences (2076-3417); Sep2024, Vol. 14 Issue 18, p8534, 41p

Schlagwörter: METAL defects, ARTIFICIAL intelligence, ALGORITHMS, MELTING, GEOMETRY

A differential privacy-preserving federated learning scheme with predictive maintenance of wind turbines based on deep learning for feature compression and anomaly detection with state assessment.

Autoren: Chen, Huan Hsu, Hsin-Yao Hsieh, Jia-You et al.

Quelle: Journal of Mechanical Science & Technology; Jul2024, Vol. 38 Issue 7, p3413-3429, 17p

Schlagwörter: DEEP learning, WIND turbines, CONVOLUTIONAL neural networks, RENEWABLE natural resources, WIND turbine efficiency, FEDERATED learning

Geografische Kategorien: TAIWAN

A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning.

Autoren: Morales, Eduardo F. Murrieta-Cid, Rafael Becerra, Israel et al.

Quelle: Intelligent Service Robotics; Nov2021, Vol. 14 Issue 5, p773-805, 33p

Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency.

Autoren: Al-Kamil, Safa Jameel Szabolcsi, Róbert

Quelle: Mathematics (2227-7390); Jun2024, Vol. 12 Issue 12, p1787, 21p

Schlagwörter: MOBILE robots, POTENTIAL field method (Robotics), TRAJECTORY optimization, MACHINE learning, ROBOTIC path planning, ROBOT control systems

Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review.

Autoren: Kim, Tongwha Behdinan, Kamran

Quelle: Journal of Intelligent Manufacturing; Dec2023, Vol. 34 Issue 8, p3215-3247, 33p

Schlagwörter: DEEP learning, MACHINE learning, INTEGRATED circuit design, CONVOLUTIONAL neural networks, SEMICONDUCTOR wafers, CLASSIFICATION

Wafer Bin Map Recognition With Autoencoder-Based Data Augmentation in Semiconductor Assembly Process.

Autoren: Shen, Po-Cheng Lee, Chia-Yen

Quelle: IEEE Transactions on Semiconductor Manufacturing; May2022, Vol. 35 Issue 2, p198-209, 12p

Schlagwörter: DATA augmentation, CONVOLUTIONAL neural networks, SYSTEM failures, SEMICONDUCTORS, PATTERN recognition systems

Anomaly Detection in Aerial Videos With Transformers.

Autoren: Jin, Pu Mou, Lichao Xia, Gui-Song et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Aug2022, Vol. 60, p1-13, 13p

Schlagwörter: INTRUSION detection systems (Computer security), VIDEO codecs, VIDEO surveillance, STREAMING video & television, DRONE aircraft, INDUSTRIAL capacity, VIDEOS

Deep Learning in High Voltage Engineering: A Literature Review.

Autoren: Mantach, Sara Lutfi, Abdulla Moradi Tavasani, Hamed et al.

Quelle: Energies (19961073); Jul2022, Vol. 15 Issue 14, pN.PAG-N.PAG, 32p

Schlagwörter: DEEP learning, HIGH voltages, TECHNICAL literature, PARTIAL discharges, MACHINE learning, ELECTRIC power systems

A survey of learning‐based robot motion planning.

Autoren: Wang, Jiankun Zhang, Tianyi Ma, Nachuan et al.

Quelle: IET Cyber-systems & Robotics; Dec2021, Vol. 3 Issue 4, p302-314, 13p